In the early 20th century, two plant ecologists proposed different theories that explained ecological communities. Frederic Clements (1916) (Fig. 1) theorized that communities were governed by laws of succession (meaning the presence of a species or group is determined by the stage of an ecosystem) and that each species had a specific role to play. In this scenario, species presence would be nonrandom and predictable. This would mean that species within communities are highly correlated. Henry Gleason (1917) (Fig. 2), however, theorized that communities were not functionally organized; instead they were random or chance associations of species. Since these two competing theories were developed, scientists have both used and challenged them in countless experiments and models. Currently, it is strongly believed that communities are highly structured and that there are strong interactions between members of a community. On the other hand, many have observed weak interactions within a community, indicating coincidental and individualistic structure. It is important to note that community structure, function, and importance are not being debated; rather, it is the structuring force (or lack thereof) and processes that are being challenged.

Fig. 1: Frederic Clements

Fig. 2: Henry A. Gleason

Technological advances in computing ability and statistical methods have allowed for the development of complex models, helping us understand natural processes. Here, researchers used a new statistical approach to model how species cluster in an environment, shedding light onto the forces determining community composition.

The Study:

In a 2011 publication, the species archetype model (SAM) was presented as a way to understanding species associations within communities. This model clusters species based on response to the environment; in this case, the response is the presence or absence of a species within an environment. With SAM, species that have a similar response to an environment are grouped into a species archetype (or original pattern from which things are copied). Researchers in this study hypothesized that if species within a community are highly connected, then they should appear together and in the same environments. If species aren’t connected, they will just appear randomly throughout an environment.

Fig. 3: This map shows the survey area located on the southern coast of Australia. Dots represent sampling sites.

The SAM was used with data from species surveys and environmental monitoring from coastal areas in southern Australia (Fig. 3). This part of Australia is heavily studied and is home to many marine protected areas; as a result, there is a lot of information about environmental parameters and species distributions. Researchers surveyed 298 and 320 sites for demersal fish (or fish that live near the bottom) and macroinvertebrate (like lobsters and urchins) assemblages, respectively (Fig. 4). This data, combined with environmental data, was used in creating models that vary in the number of species archetypes and the influence of different environmental factors. Environmental factors used in these models included temperature, nutrients, and oxygen levels, among others.

Overall, 16 different models (for both demersal fish and macroinvertebrates) were created. Without getting too far into the mathematics, each model created varied in the number of species archetypes (ranging from 1 archetype to 20 archetypes, as determined by survey data) as well as how influential environmental factors were. The models vary in their statistical strength and ability to accurately predict community composition. Statistically comparing the models determined which model was the strongest. The model comparison resulted in determining how many species archetypes exist for demersal fish and macroinvertebrates. Models also determined how many species fit into each archetype. It was found that the strongest models for demersal fish came with 6 archetypes and 4 species in each archetype. For macroinvetrebrates, the strongest model came with 8 archetypes and 3 species in each archetype.

Fig. 4: A diver completing a survey.

With the strongest model selected and the species archetypes established, researchers were able to look at the environments of their study sites and determine the probability of presence for each archetype (Fig. 5 and 6). Marine environments are not black and white – there is a gradient for many factors – and as a result, they saw quite a bit of overlap between species archetypes, even though researchers were able to define archetypes by environment. This could result in changes in species interactions and community function.

Fig. 5: This figure shows the probability of encountering each demersal fish archetype (represented by each individual graph, a-f) across the survey range.

Fig. 6: This figure shows the probability of encountering each macroinvertebrate archetype (represented by each individual graph, a-h) across the survey range.

Significance:

So what does all of this say about communities? Did they get to the bottom of this 100 year old ecological debate? Well, the results of this study don’t pick a clear winner, but they do suggest that there are groups of species (species archetypes) that are highly correlated based on environmental parameters and are nonrandom in their presence, but species interactions are not forming complex webs as theorized by Clements. The SAM approach to species assemblages looks as though it could be a useful tool in predicting the presence or absence of certain species of species archetypes. This could have implications in marine management, whether for commercial or endangered species.

As scientists, we “stand on the shoulders of giants.” We continue to build off of those who came before us and advance our knowledge of the world. Using modern methods and technology, like the SAM, allows us to test the strength and stability of these “shoulders.”

I am currently a postdoc at Keck Sciences, Claremont McKenna College. I work with Dr. Sarah Gilman, measuring and modeling energy budgets in intertidal species. I am a climate scientist and marine community ecologist and my PhD (University of Rhode Island) focused on how ocean acidification and eutrophication, alters coastal trophic interactions and species assemblages.